Ai personal fragrance consultation and fragrance selection/recommendation
AssigneeCoty Inc.
InventorsShanna Weinblatt, Yoann LeTensorer, Sophie Raviot-Derrien, Mariano Agustin Rodriguez-Fraticelli, David Joyal, Michael John Baker
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Abstract
Data is collected related to a plurality of individuals, and which is used to identify whether any of the data are probable predictors of the individuals' preference for one or more fragrances. The data collection may include passive means and may be collected and/or analyzed in real time. These predictors are then used to provide recommendations of one or more fragrances to one or more individuals on demand or based on real time data.
Claims28
A method executable by computing circuitry comprising:
- querying a fragrance database for at least one fragrance;
- querying a customer database for a set of characteristics related to an individual;
- calculating a correlation coefficient for each characteristic of the set of characteristics, the correlation coefficient representing strength of an association between the at least one fragrance and each characteristic;
- selecting a parameter from an association database containing the correlation coefficient for each characteristic, the parameter selected by identifying, based on the correlation coefficient of each characteristic, wherein a characteristic of the set of characteristics is a predictor of a preference of the individual for the at least one fragrance;
- creating a fragrance recommendation model using the parameter;
- receiving user data from a stream of data, the user data comprising characteristic attributes of a user;
- upon detection of a trigger condition, evaluating the user data using the fragrance recommendation model to predict a preference of the user for the at least one fragrance;
- and
- transmitting a recommendation message that includes identification of the at least one fragrance to a user computing device of the user.
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A system for generating fragrance recommendations comprising:
- at least one processor;
- and
- memory comprising instructions that, when executed by the at least one processor, cause the at least one processor to perform operations to:
- query a fragrance database for at least one fragrance;
- query a customer database for a set of characteristics related to an individual;
- calculate a correlation coefficient for each characteristic of the set of characteristics, the correlation coefficient representing strength of an association between the at least one fragrance and each characteristic;
- select a parameter from an association database containing the correlation coefficient for each characteristic, the parameter selected by identifying, based on the correlation coefficient of each characteristic, a characteristic of the set of characteristics that is a predictor of a preference of the individual for the at least one fragrance;
- create a fragrance recommendation model using the parameter;
- receive user data for a user from a stream of data, the user data comprising characteristic attributes of the user;
- upon detection of a trigger condition, evaluate the user data using the fragrance recommendation model to predict a preference of the user for the at least one fragrance;
- and
- transmit a recommendation message that includes identification of the at least one fragrance to a user computing device of the user.
At least one non-transitory machine-readable medium comprising instructions for generating fragrance recommendations that, when executed by at least one processor, cause the at least one processor to perform operations to:
- query a fragrance database for at least one fragrance;
- query a customer database for a set of characteristics related to an individual;
- calculate a correlation coefficient for each characteristic of the set of characteristics, the correlation coefficient representing strength of an association between the at least one fragrance and each characteristic;
- select a parameter from an association database containing the correlation coefficient for each characteristic, the parameter selected by identifying, based on the correlation coefficient of each characteristic, a characteristic of the set of characteristics that is a predictor of a preference of the individual for the at least one fragrance;
- create a fragrance recommendation model using the parameter;
- receive user data for a user from a stream of data, the user data comprising characteristic attributes of the user;
- upon detection of a trigger condition, evaluate the user data using the fragrance recommendation model to predict a preference of the user for the at least one fragrance;
- and
- transmit a recommendation message that includes identification of the at least one fragrance to a user computing device of the user.
Dependencies are inferred from the claim text.